Moreover, our ultimate goal is to aid knowledge acquisition, rather than just inducing an ontology for its own sake.

we used the same methodology and dataset as the USP paper to evaluate OntoUSP on its capability in knowledge acquisition.


A relation is often manifested as verbs and has several arguments, whereas an entity typically appears as an argument of others and has few arguments of its own.

in average, there is less information available for entities than relations.


In order to compute the dot product of the feature vectors in the exponentially high dimensional feature space, we introduce the tree kernel functions as follows: The iBTK is defined as a composite kernel consisting of a source tree kernel and a target tree kernel which measures the source and the target structural similarity respectively.

the composite kernel can be computed using the ordinary monolingual tree kernels (Collins and Duffy, 2001).


The deduction from (1) to (2) is derived according to the fact that the number of identical substructure pairs rooted in the node pairs , and , equals to the product of the respective counts.

the dBTK can be evaluated as a product of two monolingual tree kernels.


Most linguistically motivated syntax based SMT systems require an automatic parser to perform the rule induction.

it is important to evaluate the sub-tree alignment on the automatically parsed corpus with parsing errors.


When extended to supervised/discriminative framework, ITG runs even more slowly.

all attempts to ITG alignment come with some pruning method.


In MERT for DPDI, however, the error measure depends on the correct E-span rather than the E-span leading to the highest system score.

the interval boundaries are the intersections between the correct E-span and all other candidate E-spans (as shown in Figure 4(b)).


But, "VBN(killed)" is indeed separable into two finegrained tree fragments of "VBN(killed:active)" and "VBN(killed:passive)" 1 .

"VBN(killed:active)" appears 5 times with "koroshita" and 1 time with "korosareta"; and "VBN(killed:passive)" appears 1 time with "koroshita" and 3 times with "korosareta".


That is, giving a (bilingual) translation grammar and a source sentence, we are trying to construct a parse forest in the target language.

the translation results can be collected from the leaves of the parse forest.


However, in addition to rule schemata (1-6) and (10-11) they also include restriction class (A) by restricting rules to only those found in the training data 5 .

by proposition 5, the Clark and Curran parser is a context-free parser.


However, this fine-grained control is exactly what the Petrov parser does automatically.

we trained the Petrov parser twice, once on the original version of CCGbank (denoted "Petrov") and once on a version of CCGbank without these features (denoted "Petrov no feats").


If we sample smaller units rather than sentences, we have partially annotated sentences and have to use a parsing algorithm that can be trained from incompletely annotated sentences.

it is difficult to use some of probabilistic models for parsing.


In other words, we should not sample bunsetsu pairs independently from a given sentence.

we select bunsetsu pairs that have smaller margin values of outputs given by the classifier during the parsing process.


However, it is impossible to count such number when we label bunsetsus pairs.

we use the number of bunsetsus that have an annotated head.


"Support vectors" of AP means vectors which are selected in the training phase and contribute to the prediction.

8 it is very important to construct models for estimating the actual annotation cost as Haertel et al.


There are many algorithms that have such a framework, which include (Yamada and Matsumoto, 2003) for English and (Kudo and Matsumoto, 2002;Iwatate et al., 2008) for Japanese.

effective use of smaller constituents in active learning would not be limited to the specific algorithm.


The hyphenation patterns used by TeXHyphenator, which are those currently used by essentially all variants of T E X, may not be optimal for our new English and Dutch datasets.

we also do experiments with the PATGEN tool (Liang and Breitenlohner, 2008).


However, in real documents common words appear repeatedly.

the second lessstandard experimental design for which we report results restricts attention to the most common English words.


Concluding, it appears that our test language Zulu has certain features which are modelled best with either a lower or higher-order model.

the ensemble leveraged strengths of both algorithms which led to a better overall performance with a calibrated threshold.


However, we find that the reviews in the other domains contain many duplicated samples.

we re-collect the reviews from http://www.amazon.com/ and filter those duplicated ones.


The new collection is here: http://llt.cbs.polyu.edu.hk/~lss/ACL2010_Data_SSLi.zip 4 http://svmlight.joachims.org/ 5 http://mallet.cs.umass.edu/ combination methods.

we apply the ME classification algorithm for further combination and co-training.


Although the performance difference between the product rule and stacking is not significant, the product rule is obviously a better choice as it involves much easier implementation.

in the semi-supervised learning process, we only use the product rule to combine the individual classifiers.


This makes the co-training process unfeasible.

we believe that our technically-defined personal/impersonal views are more suitable for two-view learning compared to subjective/objective views.


These two sets represent the two arguments for the relations.

lDA-SP is able to capture information about the pairs of topics that commonly co-occur.


LinkLDA, however, is more flexible; rather than requiring both arguments to be generated from one of |Z| possible pairs of multinomials (β z , γ z ), Lin-kLDA allows the arguments of a given extraction to be generated from |Z| 2 possible pairs.

instead of imposing a hard constraint that z 1 = z 2 (as in JointLDA), LinkLDA simply assigns a higher probability to states in which z 1 = z 2 , because both hidden variables are drawn from the same (sparse) distribution θ r .


The combination of a well-defined probabilistic model and Gibbs sampling procedure for estimation guarantee (eventual) convergence and the avoidance of degenerate solutions.

of intensive research in recent years, the behaviour of topic models is well-understood and computationally efficient implementations have been developed.


As the complex syntactic tags on the English side are based on syntactic relations and not necessarily positional proximity, the tag for relations in a phrase like in their cultural, historical and economic relations would be exactly the same as above.

phrase extraction algorithms can conflate all constructs like in their .


Instead of using just the surface form of the word, we included the root, part-of-speech and morphological tag information into the corpus as additional factors alongside the surface form.

13 a token is represented with three factors as Surface|Root|Tags where Tags are complex tags on the English side, and morphological tags on the Turkish side.


We modify the translation model for both the conditional and the joint model by adding another factor which strongly weighs translation+transliteration options by taking the square-root of the product of the translation and transliteration probabilities.

modifying equations (8) and (11) in Model-1 and Model-2 we obtain equations (15) and (16) respectively: For the optimization of lambda parameters we hold the value of the translation coefficient λ 1 13 and the transliteration coefficient λ 2 constant (using the optimized values as discussed in section 4.2.1) and optimize λ 3 again using 2-fold optimization on all the folds as described above 14 .


For transliteration options that are TM-unknown, the p w (h, u) and p w (u) factors becomes zero and the translation model probability as given by equation (13) becomes: In such cases the λ factor cancels out and no weighting of word translation vs. transliteration  Table 6: Applying Heuristic 3 and its Combinations with other Heuristics to Model-2 occurs anymore.

of this, transliterations are sometimes incorrectly favored over their translation alternatives.


This includes longer phrases, which only match in very few sentences in the data.

those long phrases are trained to fit only a few sentence pairs, strongly overestimating their translation probabilities and failing to generalize.


For these sentences, the decoder needs the singleton phrase pairs to produce an alignment.

we retain those phrases by assigning them a positive probability close to zero.


The former, EM GI +IP, obtains 58.9/61.6-a much larger gain.

the better starting point provided by EM GI has more impact than the integer program that includes GI in its objective function.


We wanted to evaluate performance out-of-the-box because    bootstrapping a supertagger for a new language is one of the main use scenarios we envision: in such a scenario, there is no development data for changing settings and parameters.

we determined a train/test split beforehand and ran the methods exactly as we had for CCGbank.


In this setting, the number of parameters K is equal to |Y | 2 ×|X| train , where |•| denotes the cardinal and |X| train refers to the number of configurations of x t observed during training.

even in moderate size applications, the number of parameters can be very large, mostly due to the introduction of sequential dependencies in the model.


If no optimal decomposition decomposes r in at least 2 subranges, then the proof given here can be adapted by reasoning on r ′ instead of r. We now suppose that at least one of them decomposes r in at least 2 sub-ranges.

it decomposes r in We select one of these optimal decomposition by taking one such that k 1 is maximal.


Note that k is at least as large as k 1 (since [i, k 1 ] is a valid "range starting in i such that k < j and such that it anchors a bundle").


Let T be the set of edges in T (k−1) that impinge on some vertex in E \ E (k) .


It is then easy to verify that the conclusion of the partial derivation on the left-hand side of each rule has the value 3 C 4x C 2y C z, while the conclusion of the right-hand side has the value 2 C 2x C 2y C z.

each step decreases the value of a derivation tree under our assignment by the amount 1 C 2x.


Further, both sets of experiments perform evaluations of mixed sets of human-produced and machine-produced summaries, so the results may be influenced by the ease of discriminating between a human and machine written summary.

we believe it is an open question how well these features predict the quality of automatically generated summaries.


However, since current systems are extractive, such a data set is not available.

we experiment on human-written abstracts to get an estimate of the expected performance of our features on abstractive system summaries.


The state of a hidden node is related to the value of its corresponding observed node as well as the states of its neighboring hidden nodes.

the local Markov property of an MRF indicates that a variable is conditionally independent on all other variables given its neighbors: , where ne(v) is the set of neighbors of v, and cl(v) = {v} ∪ ne(v) is the closed neighborhood of v. the state of a node is assumed to statistically depend only upon its hidden node and each of its neighbors, and independent of any other node in the graph given its neighbors.


This feature does not directly affect context sentences (i.e., it affects the marginal probability of context sentences through the MRF network connections).

we do not alter this feature in comparing different features.


To name a few, they do not explicitly link the functional components of the opinions like targets, holders, or modifiers with the opinion expression; some of them do not mark opinion expression spans, none of them resolves anaphoric references in discourse.

we introduced a two level annotation scheme consisting of the sentence and expression levels, which overcomes the limitations of the existing review corpora.


Similar to the MPQA scheme, which is successfully applied to the newspaper genre, the annotation scheme treats opinions and evaluations as a com-position of functional components and it is easily extendable.

we hypothesize that the scheme can also be applied to other genres with minor extensions or as it is.


Our entropy-driven selection of syntactic contexts of a clue word is based on the following assumption: Assumption 1: In text, targets of sentiments are more diverse than sources of sentiments or other accompanying attributes such as location, time, manner, etc.

targets exhibit higher entropy than other attributes.


We optimize the discrete factor on our development set and find the optimal value is 1.

a feature "dwpp = 2" represents that the logarithm of the word posterior probability is between -3 and -2; As mentioned before, we consider error detection as a binary classification task.


The learning technique that consistently yields the best results is M5P regression trees (weka.classifiers.trees.M5P).

we report all the results in this paper using this learning method.


In contrast to most of the work on confidence estimation (Blatz et al., 2004), the features we use are not internal features of the MT system.

trustRank can be applied for a large variety of Mt approaches, from statistical-based to rulebased approaches.


Since translation rarely occurs for personal names (Chen et al., 2003), the desired NE type "LOC" would be preferred to be shared between the English NE "Lake Constance" and its corresponding Chinese NE "康斯坦茨湖".

the original incorrect type "PER" of the given English NE is fixed, and the necessity of using mapping type ratio and NE type consistency constraint becomes evident.


For example, the IC M between "康斯 坦茨湖" and "Constance Lake" is [康斯坦茨, Constance, TL] and [湖, Lake, TS], so its associated mapping type ratio will be "0.5" (i.e., 1/2).

the internal mapping is further deduced by introducing the internal mapping type Mtype and the mapping type ratio  as follows:  In the above equation, the mappings between internal components are trained from the syllable/word alignment of NE pairs of different NE types.


However, only a slight improvement (68.4% type-sensitive F-score) is obtained, as shown in Table 2.

it is conjectured that the baseline alignment model is unable to achieve good performance if those features/factors proposed in this paper are not adopted.


Here the Japanese kanji "明仁" is directly adopted as the corresponding Chinese characters (as those characters were originally borrowed from Chinese), which would be pro-nounced as "Mingren" in Chinese and thus deviates greatly from the English pronunciation of "Akihito".

it is translated neither semantically nor phonetically.


Since the Chinese NE recognizer we use is not an open source toolkit, it cannot be used to carry out semi-supervised learning.

only the English NE recognizer and the alignment model are updated during training iterations.


For example, the sentences "Bohr is a Nobel laureate" and "Einstein received the Nobel Prize" are paraphrases of the same type of facts, while the sentences "Taub earned his doctorate at Princeton University" and "he graduated from MIT" are slightly different but both describe a person's educational background.

it makes sense to group sentence patterns based on the subtopics they pertain to.


Besides, we do not know the aspect-related words in advance.

the cosine similarity between two sentences may not reflect whether they are about the same aspect.


Last but not least, their documents are event-centered while ours are entity-centered.

we can use heuristics to anchor our patterns on the subject entities.


When a question can match multiple IEPs, the longest IEP is used 2 .

instead of manually creating a list of indicative keywords, we create a set of IEPs.


However, word alignment algorithms have suboptimal accuracy, so the results would have to be checked manually.

we abandoned this idea, and instead we simply placed the different TimeML markup in the correct positions manually.


For instance, in task A, the event term and the temporal expression that denote the entities that are to be ordered may not even be directly syntactically related.

it is hard to (eClass = OCCURRENCE) and ( eTense = INF) and ( ePolarity = POS) => lRelType= AFTER (183.0/77.0) ( eTense = FI) => lRelType= AFTER (55.0/10.0) (eClass = OCCURRENCE) and ( eTense = IR-PI+INF) => lRelType= AFTER (26.0/4.0) (eClass = OCCURRENCE) and ( eTense = PC) => lRelType= AFTER (15.0/3.0) (eClass = OCCURRENCE) and ( eTense = C) => lRelType= AFTER (17.0/2.0) ( eTense = PI) => lRelType= OVERLAP (388.0/95.0) (eClass = ASPECTUAL) and ( eTense = PC) => lRelType= OVERLAP (9.0/2.0) => lRelType= BEFORE (1863.0/373.0) Here, oAdjacent is the ORDER-adjacent attribute.


Before reaching the 150 mark, we discarded 94 of the drawn examples because they were included in the training set.

our training set covers roughly 38.5% of the binary noun compound instances in recent New York Times articles.


This means that some metaphorical senses are very common.

the system would extract selectional preference distributions skewed towards such conventional metaphorical senses of the verb or one of its arguments.


As a result the system would extract selectional preference distributions skewed towards such conventional metaphorical senses of the verb or one of its arguments.

although some expressions may be fully metaphorical in nature, no selectional preference violation can be detected in their use.


The authors evaluated their system on a set of example sentences compiled from the Master Metaphor List (Lakoff et al., 1991), whereby highly conventionalized metaphors (they call them dead metaphors) are taken to be negative examples.

they do not deal with literal examples as such: essentially, the distinction they are making is between the senses included in Word-Net, even if they are conventional metaphors, and those not included in WordNet.


Besides making our thoughts more vivid and filling our communication with richer imagery, metaphors also play an important structural role in our cognition.

one of the long term goals of metaphor research in NLP and AI would be to build a computational intelligence model accounting for the way metaphors organize our conceptual system, in terms of which we think and act.


Once a frame is found that provides extensive education benefit, such as the EUROPEAN INTE-GRATION AS TRAIN JOURNEY above, a politi-cian's attempt to debunk a metaphor as inappropriate risk public antagonism, as this would be akin to taking the benefit of enhanced understanding away.

rather than contesting the validity of the metaphoric frame, politicians strive to find a way to turn the metaphor around, i.e.


Since by definition σ * 2 always chooses an entropy maximizing segment, for player 1 to choose a non-entropy maximizing segment (more or less than half the length) amounts to yielding the round to player 2, which is equivalent in terms of the resulting accepted frame to a situation where player 1 chooses an entropy maximizing segment -the same one chosen by player 2.

we need to consider only deviations with entropy maximizing frames.


the only two cases for F t 0 −1 with respect to v 1 are depicted in figure 4.


Other syntactic knowledge that may be helpful for discourse resolution could also be implicitly represented in the tree.

by comparing the common sub-structures between two trees we can find out to which level two trees contain similar syntactic information, which can be done using a convolution tree kernel.


In general, we can represent a parse tree by a vector of integer counts of each sub-tree type (regardless of its ancestors): This results in a very high dimensionality since the number of different sub-trees is exponential in its size.

it is computational infeasible to directly use the feature vector ∅( ).


With a TOP rule, we have a rule for every head, including the virtual root.

we can find anomalous rules such as TOP → root ROOT:AV ROOT:NN, where multiple categories have been parsed as ROOT.


Valency refers to both properties, e.g., verbs which allow verbal (POS) subjects (dependency).

we use the pairs of dependency relations and POS tags as the units of comparison.


Flagging individual elements Previous work scored only entire rules, but some dependencies are problematic and others are not.

our methods score individual elements of a rule.


If the i-th training sample has a larger weight, the corresponding n-grams will have more contributions to the overall score WBLEU( , ) E R .

the i-th training sample gains more importance in MERT.


The other hierarchies violate these properties to a large extent.

the genres in HGC are almost represented by a flat list with just one extra level over 32 categories.


However, the two rightmost links are not merged because there is no disagreement between the two initial alignments.

the resulting alignment reinforces the ph:f mapping, but avoids the questionable se:z link.


However, the event type and argument/role assignments in the conflict table will be included in the final output because the local confidence for the individual assignments is high.

we finally build two document-level confident-event tables: the event type table and the argument (role) table.


As noted above, we assume that the role of an entity is unique for a specific event type, although an entity can take on different roles for different event types.

if there is a conflict in the document level table, the collector will only keep the one with highest confidence, or discard them all.


The fact that the driver did not know the route elicited frequent switches to the navigation domain done by both the driver and the passenger, as can be seen in Figure 1.

we have only analysed interruption and resumption from and to the interview and navigation domains.


When designing the interview and navigation tasks that were to be carried out during the data collection, we focused on designing them so that the participants were encouraged to discuss as much as possible with each other.

the navigation instructions sometimes were hard to understand, which forced the participants to discuss the instructions and together try to interpret them.


Therefore, the navigation instructions sometimes were hard to understand, which forced the participants to discuss the instructions and together try to interpret them.

we have not analysed the workload level while interrupting and resuming topics in the navigation domain since the result might be misleading.


There are no guarantees that SARSA has reached the best policy under our feature space, and this is difficult to determine empirically.

some accuracy might be gained by considering different RL algorithms.


Then the probability for each aligned word pair is estimated as follows: In this paper, the words of collocation are symmetric and we do not determine which word is the head and which word is the modifier.

the collocation probability of two words is defined as the average of both probabilities, as in Eq.


We use two features in this paper, namely alignment probabilities and collocation probabilities.

we obtain the decision rule: Based on the GIZA++ package 1 , we implemented a tool for the improved BWA method.


the translation probabilities in a translation model, for units from parallel corpora are mainly based on the co-occurrence counts of the two units.

questions emerge: how good is the sense similarity computed via VSM for two units from parallel corpora?


Since we are using this value as a weight, following (Turney, 2001), we drop log, N and ) (r F .

(3) simplifies to: It can be seen as an estimate of ) | ( c r P , the empirical probability of observing r given c. A problem with is that it is biased towards infrequent words/features.


However, the two vectors in Equation (2) cannot be directly compared because the axes of their spaces represent different words in different languages, and also their dimensions I and J are not assured to be the same.

we need to first map a vector into the space of the other vector, so that the similarity can be calculated.


Our goal is -given a source pattern -to distinguish between the senses of its associated target patterns.

we map all vectors in target language into the vector space in the source language.


Based on Equation (10) and (11), it is easy to derive the similarity as follows:   To incorporate more information than the original similarity functions -IBM model 1 probabilities in Equation 6and naïve cosine distance similarity function in Equation 12 .

the original similarity functions are to compare the two context vectors built on full training data directly, as shown in Equation 13 where the parameters i λ (i=1,2,3) can be tuned via minimal error rate training (MERT) (Och, 2003).


The two similarity functions obtained comparable results.

in Alg2, the similarity score consists of three parts as in Equation 14 our first study is to determine which one of the above four features has the most impact on the result.


Case one is that a translation model learned from the so-far labeled data will be able to translate most of the non-trigger words in the sentence correctly.

by asking a human to translate only the trigger words, we avoid wasting human translation effort.


Furthermore, different parse tree in a forest represents different derivation and interpretation for a given sentence.

forest kernel should be more robust to parsing errors than tree kernel.


(6) holds because a common subtree by extending from ( 1 , 2 ) can be formed by taking the hyper-edge ( 1 , 2 ), together with a choice at each of their leaf nodes of simply taking the non-terminal at the leaf node, or any one of the common subtrees with root at the leaf node.

there are 1 + Δ ′ 1 , , 2 , possible choices at the j th leaf node.


(5) and Algorithm 1, we can see that each hyper-edge pair ( 1 , 2 ) is only visited at most one time in computing the forest kernel.

the time complexity for computing 1 , 2 is (| 1 | • | 2 |) , where 1 and 2 are the set of hyper-edges in forests 1 and 2 , respectively.


Let P r(σ | #) and P r(# | P ≤k (w)) denote the probability (according to some D ∈ PTD k ) that a word begins with σ and ends after observing P ≤k (w).

then Equation 4 can be rewritten in terms of conditional probability as the probability assigned to a word depends not on the observed contiguous sequences as in a Markov model, but on observed subsequences.


By definition, each M ∈ A describes a probability distribution over Σ * , and similarly defines a family of distributions.

finding the MLE of S with respect to SPD k means finding the MLE estimate of S with respect to each of the family of distributions which each M ∈ A defines, respectively.


If f is a function such that f : A → B then let f ⋄ (a) = {f (a)}.

f ⋄ : A → P(B) (note f ⋄ is not surjective).


Clearly f (S) = f (L) and thus L = L(f (S)).

by Theorem 3, L is the smallest language in L f containing S. 2 Proof: For any L ∈ L f , there is a characteristic finite sample S for L (Lemma 2).


Thus for any text t for L, there is i such that S ⊆ content(t[i]).

for any j > i, φ(t(j)) is the smallest language in L f containing S by Theorem 3 and Lemma 2.


Then, an external stimulus or signal, such as a perceived word, will result in a change of the mental state.

the external stimulus can be seen as a function being applied to v yielding as result the vector v that corresponds to the persons mental state after receiving the signal.


Thus, the external stimulus can be seen as a function being applied to v yielding as result the vector v that corresponds to the persons mental state after receiving the signal.

it seems sensible to associate with every signal (in our case: token σ) a respective function (a linear mapping, represented by a matrix M = [[σ]] that maps mental states to mental states (i.e.


Consequently, the subsequent reception of inputs σ, σ associated to matrices M and M will transform a mental vector v into the vector (vM)M which by associativity equals v(MM ).

mm represents the mental state transition triggered by the signal sequence σσ .


Instead, we might need some in-between application of simple nonlinear functions in the spirit of quantum-collapsing of a "superposed" mental state (such as the winner takes it all, survival of the top-k vector entries, and so forth).

another avenue of further research is to generalize from the linear approach.


However, though machine translation techniques have been advanced a lot, the machine translation quality is far from satisfactory, and in many cases, the translated texts are hard to understand.

the translated summary is likely to be hard to understand by readers, i.e., the summary quality is likely to be very poor.


2) The LateTrans method is deemed to be more effective than the EarlyTrans method, because the translation errors of the sentences have great influences on the summary sentence extraction in the EarlyTrans method.

in this study, we adopt the LateTrans method as our baseline method.


Assuming efficient implementation, most metrics have a linear computational complexity relative to the total number of words in a document -O(n).

mUSE total computation time, given a trained model, is also linear (at factor of the number of metrics in a combination).


Indeed, the optimal grammar would be one in which each tree pair in the training data is its own rule.

proposals for using EM for this task start with a precomputed subset of rules, and with EM used just to assign weights within this grammar.


where n c,e represents the expected count of rule c → e, and K is the total number of ways to rewrite c, we now take into account our DP(α c , P 0 (• | c)) prior in (1), which, when truncated to a finite grammar, reduces to a K-dimensional Dirichlet prior with parameter α c P 0 (• | c).

in VB we perform a variational E-step with the subprobabilities given by where Ψ denotes the digamma function.


Their model segments text into utterances and identifies records, fields and field values discussed in each utterance.

one can think of this problem as an extension of the semantic role labeling problem (Carreras and Marquez, 2005), where predicates (i.e.


Nevertheless, both in training and testing the world state is observable, and the alignment and the text are conditioned on the state during inference.

there was no need to model the distribution of the world state.


For efficiency's sake, we use only spans of length less than 15; approximately 95% of the arguments in our dataset were within 15 words of the predicate, so even with this restriction we are able to supply features for nearly all valid arguments.

the second step of our training procedure is to create a separate data point for each span of S. For each span t ∈ Spans(S), we construct a Span-HMM with a regular node generating each element of S, except that a span node generates all of t. our training data contains many different copies of each sentence S, with a different Span-HMM generating each copy.


In practice, for the automata generated in our model, minimization removes at most half the states, which is not sufficient to counteract the exponential growth.

we need to find a way to approximate a message µ(w) using a simpler automataμ(w; θ) taken from a restricted class parameterized by θ.


We know from comparative linguistics that the correct mapping will pre-serve regular phonetic relationships between the two languages (as exemplified by cognates).

each character in one language will map to a small number of characters in the other language (typically one, but sometimes two or three).


This choice capture the linguistic fact that different parts-of-speech bear distinct affix frequencies.

while we draw a single distribution G stm , we maintain separate distributions G pre|stm and G suf |stm for each possible stem part-of-speech.


An additional feature of this multilayered model structure is that each distribution over morpheme pairs is derived from the single character-level base distribution G 0 .

any character-level mappings learned from one type of morphological correspondence will be propagated to all other morpheme distributions.


Even with a large number of sampling rounds, it is difficult to fully explore the latent variable space for complex unsupervised models.

a clever initialization is usually required to start the sampler in a high probability region.


We prove that any tree transformation computed by an STAG can also be computed by an STSG using explicit substitution.

a simple evaluation procedure that performs the explicit substitution is all that is needed to simulate an STAG in a toolkit for STSGs or extended tree transducers like TIBURON by May and Knight (2006).


Finally, the 1-parameter property yields that the used substitution symbols are binary (as our substitution symbols •[•] A ).

the yield-mapping actually coincides with our evaluation.


More precisely, every extended tree transducer computes such a set, so that also this step is a characterization.

we obtain that τ is an evaluation of a tree transformation computed by an extended tree transducer, and moreover, for each extended tree transducer, the evaluation can be computed (up to a relabeling) by a STAG.


We will develop a BAR-HILLEL (Bar-Hillel et al., 1964) construction for STAG.

given a STAG G and a recognizable tree language L, we want to construct a STAG G such that In other words, we take the tree transformation τ G but additionally require the input tree to be in L. Consequently, this operation is also called input restriction.


The key observation for dynamic programming is to merge "equivalent states" in the same beam (i.e., same step) if they have the same feature values, because they will have the same costs as shown in the deductive system in Figure 1.

we can define two states j, S and j ′ , S ′ to be equivalent, notated j, S ∼ j ′ , S ′ , iff.


G. In particular, each derivation d in G has a nonempty set of corresponding derivations {d I } = π −1 (d) in G I , because fragments f in d correspond to multiple fragments f I in G I that differ only in their indexed symbols (one f I per occurrence of f in B).

the set of derivations in G is preserved in G I .


In particular, assume that we have a weighting ω on rules in G I which does not depend on the specific indices used.

any fragment f I will have a weight in G I of the form: where b is the BEGIN rule, r are CONTINUE rules, and e are END rules in the fragment f I (see Figure 1(d)).


Here s(X) denotes the total number of fragments rooted at base symbol X. for fragments f in G by where now b , r and e are non-indexed type abstractions of f 's member productions in G I and Under the weight function ω G (f ), any derivation d in G will have weight which obeys and so the posterior P (d|s) of a derivation d for a sentence s will be the same whether computed in G or G I .

provided our weighting function on fragments f in G decomposes over the derivational representation of f in G I , we can equivalently compute the quantities we need for inference (see Section 4) using G I instead.


Note that all inference methods depend on the posterior P (t|s) only through marginal expectations of labeled constituent counts and anchored local binary tree counts, which are easily computed from P (d I |s) and equivalent to those from P (d|s).

no additional approximations are made in G I over G. As shown in Table 1, our model (an allfragments grammar with the weighting scheme tent by Johnson (2002).


ments s(X i ) parented by an indexed symbol X i (see Section 3.2), and when calculating the inside and outside scores during inference, we account for the collapsed subtree tokens by expanding the counts and scores using the corresponding multiplicities.

we achieve the compaction with negligible overhead in computation.


For example, 99% of verbs are annotated as predicates in Chinese PropBank.

we can simply select any word with a part-of-speech (POS) tag of VV, VA, VC, or VE as verbal predicate.


This is abnormal since a predicate normally only allows at most one argument of each core argument role (i.e., Arg0-Arg4).

such syntactic errors should be avoidable by considering those arguments already obtained in the bottom-up parsing process.


The CoNLL-2009 shared task (Hajic et al., 2009) included both verbal and nominal SRL on dependency parsing, instead of constituent-based syntactic parsing.

the SRL performances of their systems are not directly comparable to ours.


I.e., documents from the training set and the test set map on two non-overlapping regions of the feature space.

a linear classifier f S trained on D S associates non-zero weights only with words from V S , which in turn means that f S cannot be used to classify documents written in T .


Note that each training set D l contains documents from both languages.

for a pivot p l = {w S , w T } the vector w l captures both the correlation between w S and V S \ {w S } and the correlation between w T and V T \ {w T }.


w m : Recall that W encodes the correlation structure between pivot and non-pivot words in the form of multiple linear classifiers.

the columns of U identify common substructures among these classifiers.


Experiments with language-specific settings revealed that for Japanese a smaller number of pivots (150<m<250) performs significantly better.

the reported results for Japanese can be considered as pessimistic.


Specifically, for text data in languages L 1 , ..., L s , if we represent each language as a node in a graph and connect those language pairs for which we have a bilingual dictionary, the minimum requirement is that the whole graph is connected.

as a minimum, we will need s − 1 distinct bilingual dictionaries.


possible translations of each other) to be given similar probabilities by every cross-lingual topic.

when a cross-lingual topic picks up words that cooccur in mono-lingual text, it would prefer picking up word pairs whose translations in other languages also co-occur with each other, giving us a coherent multilingual word distribution that characterizes well the content of text in different languages.


Using a smaller context not only reduces the precision, but also reduces the recall rate, which is caused by the all-zero topic assignment by the topic model for documents only containing words that are not in the vocabulary.


The uneven distribution of the annotators' attention becomes even more apparent in a comparison of the total number of fixations on the different text parts: 14 out of an average of 18 fixations per example were directed at the annotation phrase and the surrounding sentence, the text context above the annotation chunk received only 2.2 fixations on the average and the text context below only 1.3.

the eye-tracking data indicates that the textual context is not as important as might have been expected for quick and accurate annotation.


We also examined whether the need for inspecting the context increases with the complexity of the annotation phrase.

we analyzed the eye-tracking data in terms of the average number of fixations on the annotation phrase and on its embedding contexts for each complexity class (see Table 3).


We now discuss whether the findings on dependent variables from our eye-tracking study are fruitful for actually modeling annotation costs.

we learn a linear regression model with time (an operationalization of annotation costs) as the dependent variable.


As stated earlier, the role of noisy visual input in our model is as the likelihood term in a Bayesian inference about sentence form and identity.

if we denote the input obtained thus far from a sentence as I, all the information pertinent to the reader's inferences can be encapsulated in the form p(I|w) for possible sentences w. We assume that the inputs deriving from each character position are conditionally independent given sentence identity, so that if w j denotes letter j of the sentence and I( j) denotes the component of visual input associated with that letter, then we can decompose p(I|w) as ∏ j p(I( j)|w j ).


In the terms of our model's policy parameters α and β described above, non-regressive policies are exactly those with β = 0, and a policy that is faster on the left-to-right pass but does make regressions is one with a lower value of α but a non-zero β .

we tested the performance of our model on the reading of a corpus of text typical of that used in reading experiments at a range of reasonable non-regressive policies, as well as a set of regressive policies with lower α and positive β .


Unfortunately, only 8 of the 48 sentences in the corpus met this criterion.

we made single-word changes to 25 more of the sentences (mostly changing proper names and rare nouns) to produce a total of 33 sentences to read, for which every bigram did occur in the BNC.


However, the sentence in (3-b) is a non-restrictive relative clause, which does not assume the presence of a constrast set.

the context (4-a) is only used with the restrictive relative clause, and the context (4-b), where only one director is mentioned, is used as the context for the non-restrictive relative clause.


This spillover processing effect is simply a matter of modeling convenience: without it, we would have to compute Surprisal probabilities over regions rather than individual words.

the overall prefix probability can be computed as: P (w) = p,c,t P prag (p|w, t, c)P coref (c|w, t)P parser (w, t), which is then substituted in Equation (1) to get a Surprisal prediction for the current word.


As the system is not stochastic, two runs using the exact same items in the same condition will produce the same result.

we made no attempt to model by-subject variability, but we did perform by-item ANOVAs on the system output.


Chen, Gibson, and Wolf (2005) showed that sentences requiring more syntactic memory during sentence processing increased reading times, and it is widely understood that center-embedding incurs significant syntactic processing costs Gibson, 1998).

we would expect for the usage of the center-embedding memory store in an HHMM parser to correlate with reading times (and therefore linguistic complexity).


Open class words (e.g., nouns and other verbs) more commonly include new words.

one may expect reading times to differ for these two types of words.


Despite this, it makes an independent contribution to the full-data and closed-class models.

we can conclude that the average embedding depth component affects reading times -i.e., the HHMM's notion of working memory behaves as we would expect human working memory to behave.


Our annotation provides further empirical support to this claim: An entailment relation exists between the focus and reference terms in 60% of the focus-reference term pairs, and in many of the remainder, entailment holds between the terms' heads.

discourse provides relations which are many times equivalent to entailment knowledge rules and can therefore be utilized in their stead.


Second, we assume the features for the pair (u, v) are generated by a distribution depending only on whether entailment holds for (u, v).


's model attempts to determine the graph that maximizes the likelihood P (F |G) and not the posterior P (G|F ).

their model contains an edge prior P (I uv ) that has to be estimated, whereas in our model it cancels out.


It is clear that the semantic link exists between the question and its answers, even though they have totally different lexical representations.

a specially designed model may learn semantic knowledge by reconstructing a great number of questions using the information in the corresponding answers.


This typically small amount of textual information associated with the image is of limited usefulness for image indexing, organization and search.

methods which could automatically supplement the information available for image indexing and lead to improved image retrieval would be extremely useful.


The comparison shows that the Wikipedia baseline ROUGE scores (R2 .097***, RSU4 .14***) are significantly higher than the first document ones (R2 0.042, RSU4 .079) 6 .

we will focus on the Wikipedia baseline summaries to draw conclusions about our automatic summaries.


The COCONUT corpus is also limited to annotations with symbolic information about objects, such as object attributes and location in discrete coordinates.

in addition to the artificial nature of interaction, such as using keyboard input, this corpus only records restricted types of data.


SVM) should be separately created with regards to distinct features.

focusing on the difference of the pronominal usage of referring expressions, we separately create the reference resolution models; one is for identifying a referent of a given pronoun, and the other is for all other expressions.


State s is comprised of the state of the external environment E, and the state of the document (d, W ), where W is the list of all word spans mapped by previous actions.

an action a selects a span W a of unused words from (d, W ), and maps them to an environment command c. of a, the environment state changes to E ∼ p(E |E, c), and the list of mapped words is updated to W = W ∪ W a .


Moreover, the predictive strength of the properties is not known in advance.

we incorporate them as separate features in the model, and allow the learning process to estimate their weights.


In such cases, we recursively split internal nodes until the bracketing aligned, as follows: We can summarize productions more compactly by using a dependency framework and clipping off any dependents whose subtrees do not cross a bracketing boundary, relative to the parent.

dT NNP NNP VBZ dT IN dT JJS JJ NN becomes dT NNP VBZ, "the Star reports."


We take the same approach as previous work (Klein and Manning, 2004;Cohen and Smith, 2009) and use gold part-of-speech labels as tokens.

the basic observed "word" types are  Figure 1: An example of a linguistically-plausible phylogenetic tree over the languages in our training data.


With diagonal covariance in the Gaussian drift terms, each parameter evolves independently of the others.

our prior will be most informative when features activate in multiple languages.


However, because that feature does not show up in any other language, it is not usefully controlled by the prior.

we also include coarser features which activate on more abstract, cross-linguistic configurations.


Punctuation marks are very frequent in corpora and are easy to cluster.

including them in the evaluation greatly inflates the scores.


These functions have many local maxima and the specific solution to which algorithms that maximize them converge strongly depends on their (random) initialization.

their output's quality often significantly diverges from the average.


For example, it allows to disambiguate relationships between objects such as X part-of Y or X inside Y.

a coarse approximation of the height of a house and a window is sufficient to decide that in the 'house window' nominal compound, 'window' is very likely to be a part of house and not vice versa.


Usage of all this knowledge allows us to select the correct value of 1.695m and reject other values.

we can increase the precision of value extraction by finding and analyzing an entire group of comparable objects.


Comparing results for different question types we can see substantial performance differences between the attribute types.

depth shows much better overall results than width.


However, it should be noted that hypernym extraction methods in the literature do not extract hypernyms from definitional sentences, like we do, but rather from specific patterns like "X such as Y".

a direct comparison with these methods is not possible.


Upon inspecting our results, it was found that this phenomenon was due to the distinct and specific patterns, such as "treedt toe tot" ("become member of"), which linguistically realize the member-of relations in the Dutch corpus.

initializing our IE algorithm with seeds that instantiate relations other than member-of fails to detect these unique patterns, and fails to subsequently discover partwhole tuples describing the member-of relations.


Example of such patterns include "ligt in" ("lie in"), "is gelegen in" ("is located in"), and "treedt toe tot" ("become member of"), respectively describing the located-in and member-of relations.

we observed that 1) tuples harvested from  both the English and Dutch corpora by seeds instantiating a single particular type of part-whole relation highly correlated with tuples discovered by at least one other type of seeds (member-of and contained-in for English, and sub-quantityof, contained-in and structural part-of for Dutch); 2) some part-whole relations are manifested by a wide variety of specialized patterns (sub-quantityof, structural part-of, and located-in for English, and located-in and member-of for Dutch).


These factors make it difficult to produce reliable syntactic analysis outputs.

the head nouns and hence the IHs extracted therefrom are likely to be error-prone, as will be shown by our experiments in Section 6.3.


While the feedback model was unable to find any of the synonyms of the query terms, due to their lack of co-occurence with the query terms, the MultiPRF model was able to get these terms, which are introduced primarily during the backtranslation process.

terms like {genetic, gen, engineering}, which are synonyms of the query words, are found thus resulting in improved performance.


However, while the MultiPRF model has some terms pertaining to Men's Winners of Wimbledon as well, the original feedback model suffers from severe topic drift, with irrelevant terms such as {telefonbuch, telekom} also amongst the top terms.

we notice that despite the error in query translation MultiPRF still manages to correct the drift of the original feedback model, while also introducing relevant terms such as {verfecht, steffi, martina, novotna, navratilova} as well.


Thus we notice that despite the error in query translation MultiPRF still manages to correct the drift of the original feedback model, while also introducing relevant terms such as {verfecht, steffi, martina, novotna, navratilova} as well.

as shown in (Chinnakotla et al., 2010), having a better query translation system can only lead to better performance.


Here the improvements are with respect to the best model amongst the two MultiPRF models corresponding to each of the two assisting languages, with the same source language.

we observe that a simple linear interpolation of models is not the best way of combining evidence from multiple assisting languages.


It is observed that a complete opinion is always expressed in one sentence, and the relevant target of the opinion is mostly the one found in it.

it is crucial to maintain the associative information between an opinion and its target within a sentence.


'李连杰' (Jet Lee) as there were a number of similar relevant targets for the two topics, and therefore many word pairs ended up the same.

documents belonging to topic 7 and topic 8 could not be differentiated, and they both performed badly.


3.2 Delta tf.idf Martineau and Finin (2009) provide a solution to the above issue of idf utilization in a classification scenario by localizing the estimation of idf to the documents of one or the other class and subtracting the two values.

the weight of term Table 2: SMART notation for inverse document frequency variants.


Since the entity-mention model aims to classify whether an NP is coreferent with a preceding cluster, each of its training instances (1) corresponds to an NP, NP k , and a preceding cluster, C j , and (2) is labeled with either POSITIVE or NEGATIVE, depending on whether NP k should be assigned to C j .

we can represent each instance by a set of cluster-level features (i.e., features that are defined over an arbitrary subset of the NPs in C j ).


Event mentions are not only expressed as verbs in text, but also as nouns and adjectives.

for a better coverage of semantic features, we also employ the semantic annotations encoded in the FrameNet corpus (Baker et al., 1998).


For features, we make the distinction between feature types and feature values (e.g., POS is a feature type and has values such as NN and VB).

each event mention is characterized by L feature types, FT, and each feature type is represented by a finite vocabulary of feature values, f v. we can represent the observable properties of an event mention as a vector of L feature type -feature value pairs (FT 1 : , where each feature value index i ranges in the feature value space associated with a feature type.


4 Specifically, the mIBP defines a distribution over an unbounded set of binary Markov chains, where each chain can be associated with a binary latent feature that evolves over time according to Markov dynamics.

if we denote by M the total number of feature chains and by T the number of observable components, the mIBP defines a probability distribution over a binary matrix F with T rows, which correspond to observations, and an unbounded number of columns M , which correspond to features.


However, the utilization of the ACE corpus for the task of solving event coreference is limited because this resource provides only withindocument event coreference annotations using a restricted set of event types such as LIFE, BUSI-NESS, CONFLICT, and JUSTICE.

as a second dataset, we created the EventCorefBank (ECB) corpus 6 to increase the diversity of event types and to be able to evaluate our models for both within-and cross-document event coreference resolution.


One baseline groups each event mention by its event class (BL eclass ).

for this baseline, we cluster mentions according to their corresponding EC feature value.


Coreference resolution systems have been tested on languages other than English only within the ACE program (Luo and Zitouni, 2005), probably due to the fact that coreferentially annotated corpora for other languages are scarce.

there has been no discussion of the extent to which systems are portable across languages.


Similar to STRONG MATCH but following Ng and Cardie (2002)'s best link approach.

the mention under analysis is linked to the most confident mention among the previous ones, using TiMBL's confidence score.


Finally, the two schemes frequently disagree on borderline cases in which coreference turns out to be especially complex (4).

some features will behave differently, e.g., the appositive feature has the opposite effect in the two datasets.


On the Chinese side, the IP is too flat (because 台湾/Táiwān has been analyzed as a topic), whereas the more articulated structure would also be quite reasonable.

on the English side, the high attachment of the PP disagrees with the corresponding Chinese structure, but low attachment also seems reasonable: even in the gold-standard parse trees, phrase structure can be underspecified (like the flat IP above) or uncertain (like the PP attachment above).


Moreover, we allow the product categories X 1 * • • • * X n to be of any length n, and we allow the slash categories to take any number of arguments on either side.

every phrase can be assigned a (possibly very complex) syntactic category, so that fuzzy tree-to-tree extraction does not lose any rules relative to stringto-string extraction.


Experiences can be recorded around a frame like "who did what, when, where, and why" although opinions and emotions can be also linked.

attributes such as location, time, and activity and their relations must be extracted by devising a method for selecting experiencecontaining sentences based on verbs that have a particular linguistics case frame or belong to a "do" class (Kurashima et al., 2009).


As (Pantel et al., 2009) show, picking seeds that yield high numbers of different terms is difficult.

when dealing with unbounded sets of relations (Banko and Etzioni, 2008), providing many seeds becomes unrealistic.


No other study has described the use or effect of recursive patterns for different semantic relations.

going beyond (Kozareva et al., 2008;Hovy et al., 2009), we here introduce recursive patterns other than DAP that use only one seed to harvest the arguments and supertypes of a wide variety of relations.


4 is nonconvex and, as we will show in §4, hard to optimize.

it is necessary to resort to approximate algorithms like Viterbi EM.


The ECL i PS e solver also stores each set variable in a data structure that increases linearly with the number of elements, so that the size of the problem as stored by ECL i PS e is also Θ(n 2 b); but the constant for ECL i PS e appears to be much smaller, and its search algorithm stores only incremental updates (with nodes per set instead of per element) on a stack as it explores the tree.

the ECL i PS e solver can process much larger instances than CPLEX without exhausting memory.


At the heart of the mapping algorithm lies the calculation of the conditional probability p(s|w) of selecting the Word-Net sense s given the Wikipage w. The sense s which maximizes this probability can be obtained as follows: The latter formula is obtained by observing that p(w) does not influence our maximization, as it is a constant independent of s. As a result, the most appropriate sense s is determined by maximizing the joint probability p(s, w) of sense s and page w. We estimate p(s, w) as: where score(s, w) = |Ctx(s) ∩ Ctx(w)| + 1 (we add 1 as a smoothing factor).

in our algorithm we determine the best sense s by computing the intersection of the disambiguation contexts of s and w, and normalizing by the scores summed over all senses of w in Wikipedia and WordNet.


Starting from each sense s of the target word, it performs a depth-first search (DFS) of the WordNet(++) graph and collects all the paths connecting s to senses of other words in context.


Our experiments show that the large amount of knowledge injected into WordNet is of high quality and, more importantly, it enables simple knowledge-based WSD systems to perform as well as the highest-performing supervised ones in a coarse-grained setting and to outperform them on domain-specific text.

our results go one step beyond previous findings (Cuadros and Rigau, 2006;Navigli and Lapata, 2010) and prove that knowledge-rich disambiguation is a competitive alternative to supervised systems, even when relying on a simple algorithm.


Many supervised WSD systems have been built, but the effort of creating the training corpus-annotated sense marked corpora-has always been a matter of concern.

attempts have been made to develop unsupervised and knowledge based techniques for WSD which do not need sense marked corpora.


For example, the last stack in Table 1 is associated with the head centre and the tail inform(area).

bAGEL assigns non-zero probabilities to realisation phrases in unseen semantic contexts, by backing off to the head and the tail of the stack.


This allows the planner to predict the nonlinguistic context in which a later part of the utterance will be interpreted, and therefore to search for contexts that allow the use of simple REs.

the work of referring to an object gets distributed over multiple utterances of low cognitive load rather than a single complex noun phrase.


We model adjectives whose interpretation depends on the nominal phrases they modify, as well as on the non-linguistic context, by keeping track of the distractors that remain after uttering a series of modifiers.

unlike most other RE generation approaches, we are not restricted to building an RE by simply intersecting lexically specified sets representing the extensions of different attributes, but can correctly generate expressions whose meaning depends on the context in a number of ways.


Applying left removes distractor(n 1 , b 3 ) from the state.

we have reached a goal state; the complete plan decodes to the sentence "push the left button".


These propositions, which can be derived via traditional SRL analyses, should increase our confidence that c is the iarg 2 of investment in Example 5.

the unit of classification for a candidate constituent c is the three-tuple p, iarg n , c , where c is a coreference chain comprising c and its coreferent constituents.


The baseline heuristic covers the simple case where identical predicates share arguments in the same position.

it is interesting to examine cases where the baseline heuristic failed but the discriminative model succeeded.


The phrase table built in 1 has many inappropriate phrase pairs.

we filter the phrase table and keep only appropriate phrase pairs using the sigtest-filter (Johnson et al., 2007).


Building paraphrase lattices with all erroneous paraphrase pairs and decoding these paraphrase lattices caused high computational complexity.

we limited the number of paraphrasing per phrase and per sentence.


While excelling at memorizing local translation and reordering, phrase-based systems have difficulties in modeling permutations among phrases.

it is important to develop effective reordering models to capture such non-local reordering.


Based on parallel training data, a classifier is built automatically to decide whether a node in the parse trees should be used as a reordering constraint or not.

we obtain a 0.8 BLEU point improvement over a full constraint-based system.


In real corpora, cases such as node N2 are frequent enough to be noticeable (see Fox (2002) or section 4.1 in this paper).


However, the counting transducer for unigrams is simpler than the corresponding counting transducer for higherorder n-grams.

counting unigrams in E n is easier than counting n-grams in E. Associated with each E n we have a transducer Ψ n which can be used to calculate the path posterior probabilities p(u|E) for all u ∈ N n .


Previous studies (Stamatatos et al., 1999) have observed that having unequal number of words per author in the training set leads to poor performance for the authors with fewer words.

we ensured that, in the training set, the total number of words per author was roughly the same.


There is a mounting body of evidence that student self-explanation and contentful talk in humanhuman tutorial dialogue are correlated with increased learning gain (Chi et al., 1994;Purandare and Litman, 2008;.

computer tutors that understand student explanations have the potential to improve student learning (Graesser et al., 1999;Jordan et al., 2006;Aleven et al., 2001;Dzikovska et al., 2008).


While the detailed assessments of answers to open-ended questions are intended to improve potential learning, they also increase the probability of misunderstandings, which negatively impact tutoring and therefore negatively impact student learning (Jordan et al., 2009).

appropriate error recovery strategies are crucially important for tutorial dialogue applications.


Instead, regardless of the answer analysis, the system always uses a neutral acceptance and bottom out strategy, giving the student the correct answer every time, e.g., "OK. One way to phrase the correct answer is: the open switch creates a gap in the circuit".

the students are never given any indication of whether they have been understood or not.


However, in both conditions the frequency of non-understandings was negatively correlated with user satisfaction: FULL r = −0.36, p = 0.03, BASE r = −0.4, p = 0.01.

even though in BASE the system did not indicate non-understanding, students were negatively affected.


Help messages for other error types were more frequent when the expected answer was a complex sentence, and multiple possible ways of phrasing the correct answer were acceptable.

it was more difficult to formulate a prompt that would clearly describe the problem in all contexts.


For example, in the folktale The Princess and the Pea, from which (1) was extracted, there is only one king in the entire story.

in that story, the king is sufficient for identification, and therefore the modifier old is descriptive.


Past experience with corpora such as the Wall Street Journal shows that the community is eager to annotate available language data, and we anticipate even greater interest in MASC, which includes language data covering a range of genres that no existing resource provides.

we expect that as MASC evolves, more and more annotations will be contributed, thus creating a massive, inter-linked linguistic infrastructure for the study and processing of current American English in its many genres and varieties.


According to this assumption, the score function Score(⟨t, t ′ ⟩) is high when the source elementary tree t matches incorrect parse trees and the target elementary tree t ′ matches correct parse trees.

sTsG rules with high scores are regarded to be useful for error correction.


Implicit non-binary matching with NCD is indicated by preliminary experiments which show that NCD is less sensitive to random changes on the character level than, for instance, BLEU, which only counts the exact matches between word n-grams.

comparison of sentences at the character level could account better for morphological changes.


Another issue is that it operates on lemmas and it completely disregards correct word forms.

a weighted linear combination of SemPOS and BLEU (computed on the surface representation of the sentence) should compensate for this.


However, P includes also irrelevant exemplars, while for a paraphrase to be judged as good, it is sufficient that one plausible reading exists.

we use P = act(P, s) to represent the paraphrase.


In the SRL-only challenge of the task, participants are required to identify predicate-argument structures of only the specified predicates.

the problems to be solved are predicate sense disambiguation and argument role labeling.


Compiling the database is time-consuming and not easily maintainable, and the usefulness is limited by the manual collection of pre-stored suggestions.

it is beneficial if a system can mainly use untagged data from a corpus containing correct language usages rather than the errortagged data from a learner corpus.


In Table 1, we can observe that these collocations exhibit a similar discourse function and can thus become interchangeable in these sentences.

based on the outputs along with the probability from the classifier, we can provide more than one adequate suggestions.


For example, the term 'Road' appears in almost all addresses, and might be picked up as the most frequent term very early in the process.

it is beneficial to ignore marker terms during taxonomy generation and adding them as a post-processing step.


The analysis (figure 1) reveals that phrase lengths are typically between one and four words (this was true of over 95% of individuals, over 90% of classes, and over 98% of properties), as in the following random selections: Individuals: beaujolais region, beringer, blue mountains, bondi beach Classes: abi graph plot, amps block format, abattoir, abbey church Properties: has activity, has address, has amino acid, has aunt in law Our second analysis concerns axiom patterns, which we obtain by replacing all atomic terms with a symbol meaning either individual, class, property, datatype or literal.

for example the axioms Admiral Sailor and Dog Animal are both reduced to the form C A C A , where the symbol C A means 'any atomic class term'.


It is difficult, however, to imagine a context where 'chief' and 'forefront' are synonyms.

it is easy to imagine that simply replacing all occurrences of 'chief' and 'forefront' with 'head' do sometimes harm with word alignment accuracy, and we have to model either the context or senses of words.


Here we assume that the dependency edge (f → f ′ ) of word f generates target word e (we call it head word trigger in Section 4).

two words in one language trigger one word in another, which provides a more sophisticated and better choice for the target word, i.e.


Except for the wellformed structure, we could also extract girl found a beautiful house.

if the modifier The lovely changes to The cute, this rule also works.


More specifically, the probability could be estimated by the maximum likelihood (MLE) approach, Given a phrase pair f , e and word alignment a, and the dependent relation of the source sentence d J 1 (J is the length of the source sentence, I is the length of the target sentence).

given the lexical translation probability distribution p(e|f → f ′ ), we compute the feature score of a phrase pair (f , e) as represents dependent relation of the target side) in the similar way.


In fact, we just exploit the dependency structure during the rule extraction phase.

we don't need to change the main decoding algorithm of the SMT system.


However, given that the segmentation of the story was obtained by an automatic sentence splitter, there is no reason to attempt to reproduce this segmentation.

for quantitative evaluation purposes we follow Noh et al.


Coreference resolution is a complex problem, and successful systems must tackle a variety of non-trivial subproblems that are central to the coreference taske.g., mention/markable detection, anaphor identification -and that require substantial implementation efforts.

many researchers exploit gold-standard annotations, when available, as a substitute for component technologies to solve these subproblems.


The dependency relation between the target trees is represented by the three words w i , MPHC, and w j .

the features are designed to incorporate these words, their relational words, and the three words next to w j .


Tri-training, explained below, is a semi-supervised learning method which requires large amounts of data.

we only used very fast learning algorithms in the context of tri-training.


It is not smooth, making it unamenable to gradient-based optimization algorithms.

we use a smoothed approximation, where 0 < β ≤ 1 (Mohimani et al., 2007).


However, for a model which is a product of multinomial distributions, the L1 norm is a constant.

we cannot use the L1 norm as part of the size term as the result will be the same as the EM algorithm.


However the sampler used for parsing was biased because it used as its proposal distribution a truncated grammar which excluded all but a handful of the unseen elementary trees.

the proposal had smaller support than the true model, voiding the MCMC convergence proofs.


Some other English NPIs are 'ever', 'yet' and 'give a damn'.

nPIs can be treated as clues that a DE operator might be present (although DE operators may also occur without nPIs).


However, NPI-hood need not be preserved under translation [17].

for most languages, we lack the critical clues that DLD09 depends on.


Even though, as discussed above, the translation of an NPI need not be an NPI, a preliminary review of the literature indicates that in many languages, there is some NPI that can be translated as 'any' or related forms like 'anybody'.

with a small amount of effort, one can form a minimal NPI seed set for the DLD09 method by using an appropriate target-language translation of 'any'.


Some examples are: 'abţinut' (abstained), 'criticat' (criticized) and 'reacţionat' (reacted).

a significant fraction of the DE operators derived by our co-learning algorithm would have been missed by the translation alternative even under ideal conditions.


We therefore hypothesize that the choice of vocabulary is not only a matter of topic but also of perspective, while word recurrence has mainly to do with the topical composition of the text.

tracing word frequencies is not going to be effective for perspective classification beyond noting the mere presence/absence of words, differently from the findings in text categorization, where frequency-based features usually do better than boolean features for sufficiently large vocabulary sizes (McCallum and Nigam, 1998).


The effectiveness of a small subset of features is consistent with the observation in the discourse analysis studies that rivals in long-lasting controversies tend to consolidate their vocabulary and signal their perspective with certain stigma words and banner words, that is, specific keywords used by a discourse community to implicate adversaries and to create sympathy with own perspective, respectively (Teubert, 2001).

in abortion debates, using infanticide as a synonym for abortion is a pro-life stigma.


When N best features are eliminated, performance goes down significantly with even smaller N for PBA and BL datasets.

top features are not only effective, they are also crucial for accurate classification, as their discrimination capacity is not replicated by any of the other vocabulary words.


Remind that neither of these models alone fully solve the joint task of extracting boundaries as well as determining two attributions simultaneously.

when conjoining the results from the two models (BASELINE-1), the final performance drops substantially.


Up to A k , we get a list of candidates T : T 1 , T 2 , ..., T L , ranked by probabilities P k (T |S) in descending order.

the probability P k (t l |S)(l = 1, 2, ..., L) is accumulated probability of P (t l |S) over A 1 , A 2 , ..., A k , calculated by: If we continue performing the CRF conversion to cover all N (N ≥ k) segmentations, eventually we will get: If Equation 3 holds, then for ∀i = 1, P (t 1 |S) > P (t i |S)(i = 1), and t 1 maximizes the probability P (t |S).


Guided by the given key phrases, people can easily understand what a document describes, saving a great amount of time reading the whole text.

automatic key phrase extraction is in high demand.


They are both elaborated to reflect the content of a document.

phrases in the titles are often appropriate to be key phrases.


However, this approach lacks of an importance propagation process.

it has the highest recall value but the lowest precision.


Feedback behaviour, both in speech and gestures, can be observed especially in the person who is receiving the instructions (the follower).

we decided to focus our analysis only on the follower's part of the interaction.


Since we attempt to summarize reviews, we assume that a good summary must involve as many sentiments as possible.

we define the informativeness score as follows: where e indicates sentiment e = a, p as the tuple of aspect a and polarity p = {−1, 0, 1}, E(S) is the set of sentiments contained S, and f (e) is the score of sentiment e. Aspect a represents a standpoint for evaluating products and services.


If the corpus consists of reviews, it is expected that this approach would be effective for sentiment summarization.

we adopt and improve Lapata's approach to order sentences.


Since we attempt to order a set of sentences, the operation is regarded as solving the Traveling Salesman Problem; that is, we locate the path that offers maximum score through all n sentences as s 0 and s n+1 are starting and ending points, respectively.

the operation is NP-hard and it is difficult to find the global optimal solution.


Note that our method learns w from texts automatically annotated by a POS tagger and a named entity tagger.


The argmax operation in Equation 1 also involves search, which is NP-hard as described in Section 2.2.

we adopt the Held and Karp Algorithm and beam search to find approximate solutions.


Yang, Lin & Chen realized that bloggers tend to emphasize their feelings by using emoticons (such as: ☺," and #) and that these emoticons frequently appear in final sentences.

they first focused on the last sentence of posts as representing the polarity of the entire posts.


Although tempted to extend the experiments in order to acquire more data, longer times result in subject impatience, which shows on lower scoring rates.

we chose to trade sample size for accuracy.


In contrast, rating whole customer-reviews as compared to rating second sentences of these reviews, showed a considerable difference.

instead of focusing on whole texts, computational linguists should focus on the last sentences for efficient and accurate automatic polarity-classification.


In general, such short text spans cannot fully incorporate the contextual information relevant to the detection of subjective language (Wilson et al., 2005a).

we conjecture that good rationales should extend beyond short phrases.


Unfortunately, domain shift as well as task mismatch could be a problem with any opinion utility based on supervised learning.

3 we next consider an approach that does not rely on supervised learning techniques but instead explores the use of a manually constructed polarity lexicon.


Since spy examples are from P and are put into U in building the NB classifier, they should behave similarly to the hidden positive cases in U.


We now take the best link selection criteria, which is the confidence  (Papineni et al., 2002) and METEOR (Lavie and Agarwal, 2007) also shows improvement over baseline and approaches gold standard quality.

we achieve 45% of the possible improvement by only using 20% elicitation effort.


The user answers "Yes" or "No," indicating whether or not w i belongs to G. w i is added to the positive set p or the negative set n based on the user's answer.

we have a labeled data set that grows from iteration to iteration as the user labels each candidate word.


Third, whether active learning is used (+/-).

logistic regression using Moby and no active learning is L(M,-).


Second, the terms from the source matrix A are identified in the initial pool of sentences and those terms whose salience score is above the salience threshold are copied across to the TSR.

the TSR is formed by the most (globally) salient terms from each one of the sentences.


In order to constrain the exhaustive attachments of function words, we limit to bind them to the nearby syntactic chunks yielded by a target dependency parser.

the proposed approach can not only capture source-tree-to-target-chunk correspondences but can also use forest structures that compactly encode an exponential number of parse trees to properly generate target function words during decoding.


On the one hand, there is a tendency to integrate richer syntactic information into a translation rule in order to better express the translation phenomena.

flat phrases (Koehn et al., 2003), hierarchical phrases (Chiang, 2005), and syntactic tree fragments (Galley et al., 2006;Wu et al., 2010) are gradually used in SMT.


However, the current dominant research only uses 1-best trees for syntactic realignment (Galley et al., 2006;May and Knight, 2007;Wang et al., 2010), which adversely affects the rule set quality due to parsing errors.

we realign target function words to a packed forest that compactly encodes exponentially many parses.


Specially, we observed that most incorrect or ambiguous word alignments are caused by function words rather than content words.

we focus on the realignment of target function words to source tree fragments and use a dependency parser to limit the attachments of unaligned target words.


The labels in the output sequence y were obtained by selecting the most productive 150 stems, and then collapsing certain vowels into equivalence classes corresponding to Finnish vowel harmony patterns.

variants -kö and -ko become vowel-generic enclitic particle -kO, and variants -ssä and -ssa become the vowel-generic inessive case marker -ssA, etc.


The idea behind active learning is quite intuitive: instead of annotating a large number of randomly picked instances we carefully select a small number of instances that are maximally informative for the machine learning classifier.

a smaller set of data points is able to boost classifier performance and to yield an accuracy comparable to the one obtained when training the same system on a larger set of randomly chosen data.


Further problems arise in the AL scenario where the instances to be annotated are selected as a function of the sampling method and the annotation judgements made before.

beigman and Klebanov beigman (2009)'s approach of identifying unreliably annotated instances by disagreement is not applicable to AL, as most instances are annotated only once.


Type b) models categories in the data set which are intrinsically hard to disambiguate, while others are not.

type b) errors are of greater interest to us, as it is safe to assume that intrinsically ambiguous categories will lead to biased coder decisions and result in the systematic annotation noise we are interested in.


First, there is the risk of overfitting S. Second, we know that classifier accuracy in the early phase of AL is low.

using classifier predictions at this stage to accept or reject new instances could result in poor choices that might harm the learning proceess.


Without loss of generality, we set the maximum number of rules applicable to a word pair to be a fixed number.

the number of possible transformations for a word pair is finite, and usually limited.


A necessary condition for this is that the feature expectations do not vary significantly across domains.

our approach can be regarded as minimizing a coarse approximation of the discrepancy distance.


For a more restricted class of classifiers which rely only on any single feature 2 z i , the distance is equal to the maximum over the change in the distributions P (z i ).

for arbitrary linear classifiers we have: It follows that low inter-domain variability of the marginal distributions of latent variables is a necessary condition for low discrepancy distance.


Define an index set I = V ∪ E. A derivation is represented by a vector y = {y r : r ∈ I} where y v = 1 if vertex v is used in the derivation, y v = 0 otherwise (similarly y e = 1 if edge e is used in the derivation, y e = 0 otherwise).


4 We now give a Lagrangian relaxation algorithm for integration of a hypergraph with a bigram language model, in cases where the hypergraph satisfies the following simplifying assumption: Assumption 4.1 (The strict ordering assumption.)

for any two leaves v and w, it is either the case that: 1) for all derivations y such that v and w are both in the sequence l(y), v precedes w; or 2) for all derivations y such that v and w are both in l(y), w precedes v. under this assumption, the relative ordering of any two leaves is fixed.


For example, Inverted List (IL) which is a primary key-term access method would return a very large set of docs for a query document, which leads to the time-consuming post-processing.


If we represent each object as Equation 4, the l-th hash function is then defined as: where w ∈ R M is the feature weight to be determined and d l is the l-th column of the matrix D. Intuitively, the "contribution" of a specific feature to different classes is different.

we hope to incorporate this side information in S 3 H for better hashing.


have proven that hash functions with maximum entropy must maximize the variance of the hash values, and vice-versa (Wang et al., 2010b).

h( (•)) can be estimated over the labeled and unlabeled data, X L and X U .


Unfortunately, direct solution for above problem is non-trivial since Equation (7) is not differentiable.

we relax the objective and add an additional regularization term which could effectively avoid overfitting.


However, we do not hope that a small fraction of object-pairs with very large projection magnitude or margin dominate the complete model.

a piece-wise linear function ψ(•) is applied in S 3 H. As a result, Equation 8is a simply unconstrained optimization problem, which can be efficiently solved by a notable Quasi-Newton algorithm, i.e., L-BFGS (Liu et al., 1989).


For description simplicity, only attraction set Θ a is considered and the extension to repulsion set Θ r is straightforward.

the gradient of L(w) is as follows: Note that ∂ψ(t)/∂t = 0 when |t| > T g .


After analysis, we find: 1) hyperplanes are randomly generated and we only need to determine which sides of these hyperplanes the given object lies on, and 2) in real-world applications, objects such as docs are always very sparse.

we can avoid heavy computational demands and efficiently generate fingerprints for objects.


S 3 H uses similarity while LDA uses distance.

the objective function of S 3 H is just the reciprocal of LDA's.


This is because few objects can be hashed by SH into one hash bucket.

for many queries, SH fails to return any neighbor even in a large Hamming radius of 3.


Note that, since there are totally over 800K features, it is extremely inefficient to train SSH.

we only compare our S 3 H with IL, KLSH and SH.


(2010) take a bag-of-words approach, and assume independence between both the query terms and the corresponding annotation symbols.


The reason is that all of their documents are similar to the two document instances in target domain despite the fact that the documents of q s3 correspond to their counterparts from different target queries.

we should consider the source query as a whole and directly measure the query importance.


The basic idea of instance weighting is to put larger weights on source instances which are more similar to target domain.

the key problem is how to accurately estimate the instance's weight indicating its importance to target domain.


In order to compare query weighting with document instance weighting, we need to fairly apply them for the same approach of ranking.

we choose pairwise approach to incorporate query weighting.


The task of HP and NP are more similar to each other whereas HP/NP is rather different from TD (Voorhees, 2003;Voorhees, 2004).

we carried out HP/NP to TD and TD to HP/NP ranking adaptation tasks.


By using the inverse rank, we only take into account the relative ranking of base entries and ignore their absolute scores.

note that the score of a base entry depends on a review d. we select different base entries as additional features for expanding different reviews.


For example, while it learns that wonderful and amazing are semantically close, it doesn't capture the fact that these are both very strong positive sentiment words, at the opposite end of the spectrum from terrible and awful.

we extend the model with a supervised sentiment component that is capable of embracing many social and attitudinal aspects of meaning (Wilson et al., 2004;Alm et al., 2005;Andreevskaia and Bergler, 2006;Pang and Lee, 2005;Goldberg and Zhu, 2006;Snyder and Barzilay, 2007).


According to the latest Twitter entry in Wik-ipedia, the number of Twitter users has climbed to 190 million and the number of tweets published on Twitter every day is over 65 million 1 .

of the rapidly increasing number of tweets, mining people's sentiments expressed in tweets has attracted more and more attention.


In particular, we ask our annotators what is the pragmatic function of each utterance within the dialogue, a decision that often depends upon how earlier utterances were classified.

augmented, DA tags become an important source of evidence for detecting language uses and such social phenomena as conformity.


T/txt-frag, 'a text unit consisting of 2 or more subanalyses that cannot be combined using any rule in the grammar').

we believe that many (longer-distance) grammatical constructions and errors found in texts can be (implicitly) captured by this feature type.


As a comparison, evaluating each human description as a paraphrase for the other descriptions in the same cluster resulted in a BLEU score of 52.9 and a PINC score of 77.2.

all the systems performed very well in terms of retaining semantic content, although not as well in producing novel sentences.


In order to measure correlation, we need to score each paraphrase individually.

we recomputed BLEU on the sentence level and left the PINC scores unchanged.


How-ever, a significant portion of the remaining data is useful.

an automatic method for filtering those sentences could allow us to utilize even more of the data.


Since only one pair of (d, w) is being observed, as a result, the joint probability model is a mixture of log-linear model with the expression Typically, the number of documents and vocabulary size are much larger than the size of latent semantic class variables.

latent semantic class variables function as bottleneck variables to constrain word occurrences in documents.


Instead, we have access to a supercomputer at a supercomputer center with MPI installed that has more than 1000 core processors usable.

we implement our algorithms using C++ under MPI on the supercomputer, where we have to write C++ codes for Map part and Reduce part, and the MPI is used to take care of massage passing, scheduling, synchronization, etc.


The IBM Model-1 assumes one target word can only be aligned to one source word.

given a target word we can always identify which source word it is aligned to.


incorrect C precision , P precision , C recall and P recall are the sum of the fractional counts of correctly or partially translated semantic frames in the MT output and the reference, respectively, which can be viewed as the true positive for precision and recall of the whole semantic structure in one source utterence.

the SRL based MT evaluation metric is equivalent to the f-score, i.e., the translation accuracy for the whole predicate-argument structure.


Translation into languages with rich morphology presents special challenges for phrase-based methods.

birch et al (2008) find that translation quality achieved by a popular phrase-based system correlates significantly with a measure of targetside, but not source-side morphological complexity.


The score-updating function u s (µ) computes those model estimates which can be completed using a message in the set µ and returns the difference between the new and old scores.

of translating into a morphologically rich language, some inflected forms of target words are unobserved in training data and cannot be generated by the decoder under standard phrasebased approaches.


The corpus comes with automatically generated annotation and a randomized split into training, development, and testing sets.

the annotation for the development and testing sets provides a realistic reflection of what could be obtained for arbitrary source text.


Doc Because topic models treat the document context as exchangeable, a document is a natural context for partial state ablation.

if a user adds a set of words S to constraints, then we have reason to suspect that all documents containing any one of S may have incorrect topic assignments.


None The final option is to move words into constraints but keep the topic assignments fixed.

p and W change, but not T , as described in Algorithm 2.


Of the 31 million queries, only about 1 million were unique.

we expect that keeping the results of language model queries in a cache should be effective at reducing overall language model latency.


In complex domains, finding a winning strategy is challenging even for humans.

human players typically rely on manuals and guides that describe promising tactics and provide general advice about the underlying task.


In the second layer of the neural network, the units z represent a predicate labeling e i of every sentence y i ∈ d. However, our intention is to incorporate, into action-value function Q, information from only the most relevant sentence.

in practice, we only perform a predicate labeling of the sentence selected by the relevance component of the model.


This however, is  impractical since the relevance decision is dependent on the game context, and is hence specific to each time step of each game instance.

for the purposes of this evaluation, we modify the game manual by adding to it sentences randomly selected from the Wall Street Journal corpus (Marcus et al., 1993) -sentences that are highly unlikely to be relevant to game play.


Stereotypes-in-a-neighborhood: if ?X matches any of {X, X 1 , X 2 , …, X n } then @?X matches any of @X ∪ @X 1 ∪ … ∪ @X n .

@?corpse matches any salient property of any stereotype in the neighborhood of corpse, while @?fast matches any stereotype noun with a salient property that is similar to, and reinforced by, fast.


The more wildcards a query contains, the more degrees of freedom it offers to the explorer.

the query "?scientist 's ?laboratory" uncovers a plethora of analogies for the relationship between scientists and their labs: matches in the Google 3-grams include "technician's workshop", "artist's studio", "chef's kitchen" and "gardener's greenhouse".


Each histogram dl j i carries information about the distribution of terms at a certain position µ j of the document, where σ determines how the nearby terms to µ j influence the local histogram j.

sequential information of the document is considered throughout these local histograms.


When using words (columns 2-6 in Table 5) the differences in performance are of 15.6%, 6.2%, 6.8%, 2.9%, 3.8% when using 1, 3, 5, 10 and 50 documents per author, respectively.

it is evident that local histograms are more beneficial when less documents are considered.


[…] How well a particular word is known may condition the connections made between that particular word and the other words in the mental lexicon.

instead of modeling when a particular word will become fully known, it makes more sense to model the degree to which a word is known at different levels of language exposure.


Comparing vectors across different LSA spaces is less straightforward, since the individual dimensions in LSA do not have a meaningful interpretation, and are an artifact of the content and ordering of the training corpus used.

direct com-parisons across two different spaces, even of the same dimensionality, are meaningless, due to a mismatch in their coordinate systems.


On average, we would expect more difficult texts to contain more difficult words.

the correlation between text difficulty and our word maturity metric can serve as another validation of the metric.


(This is related to but distinct from the merely polysemous words that have several related meanings), Because of the conflation of several unrelated meanings into the same orthographic form, homographs implicitly contain more semantic content in a single word.

one would expect the meaning of homographs to mature more slowly than would be predicted by frequency alone: all things being equal, a learner has to learn the meanings for all of the senses of a homograph word before the word can be considered fully known.


Intuition about language learning, however, tells us that with enough language exposure a learner learns virtually all there is to know about any particular word; after the word reaches its adult maturity, subsequent encounters of natural readings do little to further change the knowledge of that word.

if word maturity were tracking something similar to real word knowledge, one would expect the word maturity for most words to plateau over time, and subsequently not change significantly, no matter how sophisticated the reference model becomes.


Unlike the Turkers, our student volunteers are not offered a monetary reward.

we consider their judgements to be more honest than those obtained via AMT.


Though there are many annotated data sets for the research of speech summarization and sentiment analysis, there is no corpus available for opinion summarization on spontaneous speech.

for this study, we create a new pilot data set using a subset of the Switchboard corpus (Godfrey and Holliman, 1997).


However, since the size of the corpus is small, some topics only contain a few conversations, and some words only appear in one conversation even though they are topicrelevant.

the current PMI measure cannot properly measure a word's and a sentence's topic relevance.


For instance, in the contention on "investigation result of the Cheonan sinking incident", the government of North Korea and that of South Korea are the key opponents; other disputants, such as politicians, experts, civic group of South Korea, the government of U.S., and that of China, mostly speak about the key opponents.

it is effective to analyze where the disputants stand regarding their attitude toward the key opponents.


We observed that the major components which shape an article on a contention are quotes from disputants and journalists" commentary.

our method considers two points for classification: first, from which side the article"s quotes came; second, for the rest of the article"s text, the similarity of the text to the arguments of each side.


We set 0.7 and 0.6 for the two parameters α and β respectively.

for an article written purely with quotes, the article is classified to a specific side if more than 70% of the quotes are from that side.


For a few issues, there were some participants who do not belong to either side; however, they usually did not take a particular position nor make strong arguments.

the effect on classification performance was limited.


Even when they discuss the same properties, their experiences and, subsequently, evaluations of the product can differ dramatically.

information in any single review may not provide a complete and balanced view representative of the product as a whole.


This is due to the limitations of the training data: we obtain the ill-formed words and their standard forms from the SMS corpus, but the ill-formed words in the SMS corpus are not sufficient to cover those in the Twitter data (and we don't have sufficient Twitter data to train the SMT method directly).

novel ill-formed words are missed in normalisation.


The dictionary lookup method ("DL") unsurprisingly achieves the best precision, but the recall on Twitter is not competitive.

the Twitter normalisation cannot be tackled with dictionary lookup alone, although it is an effective preprocessing strategy when combined with more ro-   (Cook and Stevenson, 2009); MT = SMT (Aw et al., 2006); DL = dictionary lookup; WS = word similarity; CS = context support) bust techniques such as our proposed method, and effective at capturing common abbreviations such as gf "girlfriend".


However, not all words in a tweet are closely related to the topic of that tweet; some are background words commonly used in tweets on different topics.

for each word in a tweet, the user first decides whether it is a background word or a topic word and then chooses the word from its respective word distribution.


One challenge with Twitter is the so-called echo chamber effect: when a topic becomes popular, or "trends," it quickly dominates the conversation online.

some events may have only a few referent messages while other more popular events may have thousands or more.


marginals for this distribution by using the forwardsbackwards algorithm on the potentials defined in the update.

computing the q(y) update amounts to re-running forward backwards on the message where there is an expected potential term which involves the belief over other variables.